Method for sex sorting of mosquitoes and apparatus therefor

ABSTRACT

Method and apparatus for mechanical sex-sorting of mosquitoes by extracting a class of mosquitoes from unsorted mosquitoes comprises obtaining unsorted mosquitoes, obtaining images of individual mosquitoes in a stationary phase, electronically classifying the individuals from the images into male mosquitoes and/or female mosquitoes, and possibly also unclassified objects; obtaining co-ordinates of individuals of at least one of the male mosquito and female mosquito classifications, and using a robot arm to reach an individual identified by the obtained coordinates to store or remove the individuals, thereby to provide sex-sorted mosquitoes.

FIELD AND BACKGROUND OF THE INVENTION

The present invention, in some embodiments thereof, relates to sexsorting of mosquitoes and, more particularly, but not exclusively, to amethod and apparatus for providing sorted male mosquitoes.

To date, mosquito SIT (Sterile Insect Technique) has proved itself insmall scale projects around the globe, as a promising and effective toolfor fighting mosquito-borne diseases. The idea is to release malemosquitoes with unique characteristics such that their off spring do notevolve, or the distributed males are sterile such that there will not beany offspring.

Small scale pilots, in which a few million male mosquitoes were releasedon small area, were performed by different research institutions andcompanies, all demonstrated reduction in mosquito population in thatarea weeks after deploying the engineered male mosquitoes on a weeklybasis.

In order to treat large areas, millions of mosquitoes are needed to beproduced on a daily basis.

However, the operational costs associated with the labor intensiveinvolved in the rearing and handling process today prevent the mosquitoSIT from scaling up. The specific steps in the rearing process we arereferring to are:

-   -   1. The sex sorting procedure. It is required to release males        only, as opposed to releasing females when the target goal is to        replace the local population and create a new type of mosquito        population. The mosquitoes need to be sorted between females and        males, and only males should be in the release boxes sent to the        field for release.    -   2. The loading of the mosquitoes into the release boxes. Today,        the common procedure is to place pupa (together with water)        inside the release boxes. As each release box may contain in the        order of 700-1500 mosquitoes, the workers need to transfer the        pupa from other larger containers, and measure the quantity        transferred, although resolution to a single mosquito is not        required. Transferring 1,000 pupa into a release box at a time        when millions of pupa per day are needed is highly intensive        work requiring time and people. The costs are too high for large        scale operations.

There is on-going research to optimize the sorting process. However,most systems to date, have tried to sort while in the pupa stage (basedon weight, size, color). Other attempts have involved zapping the femalemosquitoes while they are adults.

A problem is that the adult mosquito can be very active, and thus fromthe moment you classified its sex until you manage to do something itmay have moved or flown away.

Also having other mosquitoes in the field of view may obscure the visionsensor. Sorting at the pupa, may lead to large collateral damage(meaning many dead males).

SUMMARY OF THE INVENTION

The present embodiments may use a time when adult mosquitoes are still,in order to apply imaging and then pick the wanted insects or zap theunwanted insects. Such a time may be the time when the insect emergesfrom the pupa, or when the insects are cooled down to a temperature atwhich they are still. In both cases the insects are still for a periodof time which is long enough to be identified by the imaging process andthen either picked up or zapped as the imaging process is able to guidea robot arm to find the insect.

According to an aspect of some embodiments of the present inventionthere is provided a method for mechanical sex-sorting of mosquitoes byextracting a class of mosquitoes from unsorted mosquitoes, the methodcomprising:

obtaining said unsorted mosquitoes;

obtaining images of individuals of said unsorted mosquitoes in astationary phase;

electronically classifying said individuals from said images into atleast one member of a group of classifications including malemosquitoes, female mosquitoes, and unclassified objects;

obtaining co-ordinates of individuals of at least one of said malemosquito and female mosquito classifications;

using a robot arm to reach an individual identified by ones of saidobtained coordinates to store or remove said individuals, thereby toprovide sex-sorted mosquitoes.

In an embodiment, said classifying comprises using a trained neuralnetwork.

In an embodiment, said trained neural network comprises four or morelayers.

In an embodiment, said obtaining images comprises obtaining successiveframes, generating differences between said successive frames and usingsaid differences to determine which individuals are in said stationaryphase.

In an embodiment, said classifying comprises using a recurrent neuralnetwork (RNN).

In an embodiment, said unsorted insects are emerging pupae and saidstationary phase is emergence.

In an embodiment, said unsorted insects are adults and said stationaryphase is obtained by cooling said insects.

The method may comprise tracking movement of individual insects toupdate respective obtained coordinates prior to using said robot arm.

The method may comprise obtaining said images for classification using afirst, relatively high resolution, camera, and carrying out saidtracking using a second, relatively low resolution, camera.

In an embodiment, said obtained coordinates are of said male class andsaid identified individuals are picked off and placed in storage.

In an embodiment, said robot arm comprises a suction device or a blowerdevice to pick off said identified individuals and place in storage.

In an embodiment, said obtained coordinates are of said female class andsaid identified individuals are destroyed.

In an embodiment, said robot arm comprises a zapper for destroying saididentified individuals.

In an embodiment, said zapper is one member of the group comprising anelectrode and a laser.

In an embodiment, if an individual is not classified into male or femaleby a predetermined time, then the image is sent to an operator.

In an embodiment, if an individual is not classified as male by apredetermined time then it is classified as female.

In an embodiment, said insects are cooled in a container having walls,so that the cooled insects stand on an interior side of said walls, themethod comprising dismantling said box to present said interior sides ofsaid walls for said obtaining said images.

In an embodiment, said insects are cooled in a container having a trapdoor and said trap door is opened onto a first moving conveyor, to allowsaid cooled insects to fall through, said first moving conveyor carryingsaid insects to an imaging location for said obtaining images, and saidconveyor stopping with insects at said imaging location to obtain saidcoordinates.

In an embodiment, said first moving conveyor is a relatively fast movingconveyor, thereby to prevent piling of insects disrupting imaging,wherein said obtained coordinates are of the female class so that maleinsects are retained on the conveyor, the first moving conveyor emptyingonto a second moving conveyor being a relatively slow moving conveyor,said relatively slow moving conveyor conveying said retained insects forplacing in storage cartridges.

According to a second aspect of the present invention there is providedapparatus for mechanical sex-sorting of mosquitoes by extracting a classof mosquitoes from unsorted mosquitoes, the apparatus comprising:

a source of unsorted mosquitoes;

a camera configured to find individual mosquitoes on a stationary phaseand obtain images and coordinates of said individuals;

a classifier, configured to electronically classify said individualsfrom said images into at least one member of a group of classificationsincluding male mosquitoes, female mosquitoes, and unclassified objects;

a robot arm connected to said classifier and configured to reach anindividual identified by ones of said obtained coordinates to store orremove said individuals, thereby to provide sex-sorted mosquitoes.

Unless otherwise defined, all technical and/or scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of embodiments of the invention, exemplarymethods and/or materials are described below. In case of conflict, thepatent specification, including definitions, will control. In addition,the materials, methods, and examples are illustrative only and are notintended to be necessarily limiting.

Implementation of the imaging and directing of the robot arms inembodiments of the invention can involve performing or completingselected tasks manually, automatically, or a combination thereof.Moreover, according to actual instrumentation and equipment ofembodiments of the method and/or system of the invention, severalselected tasks could be implemented by hardware, by software or byfirmware or by a combination thereof using an operating system.

For example, hardware for performing selected tasks according toembodiments of the invention could be implemented as a chip or acircuit. As software, selected tasks according to embodiments of theinvention could be implemented as a plurality of software instructionsbeing executed by a computer using any suitable operating system. In anexemplary embodiment of the invention, one or more tasks according toexemplary embodiments of method and/or system as described herein areperformed by a data processor, such as a computing platform forexecuting a plurality of instructions. Optionally, the data processorincludes a volatile memory for storing instructions and/or data and/or anon-volatile storage, for example, a magnetic hard-disk and/or removablemedia, for storing instructions and/or data. Optionally, a networkconnection is provided as well. A display and/or a user input devicesuch as a keyboard or mouse are optionally provided as well.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

Some embodiments of the invention are herein described, by way ofexample only, with reference to the accompanying drawings andphotographs. With specific reference now to the drawings in detail, itis stressed that the particulars shown are by way of example and forpurposes of illustrative discussion of embodiments of the invention. Inthis regard, the description taken with the drawings makes apparent tothose skilled in the art how embodiments of the invention may bepracticed.

In the drawings:

FIG. 1 is a simplified flow chart of a first embodiment of the presentinvention using a stationary phase to image and classify mosquitoes andthen sort accordingly;

FIG. 2 is a simplified diagram showing an embodiment in which cooledmosquitoes are poured onto a conveyor for imaging in a stationary phaseaccording to FIG. 1;

FIGS. 3 to 7 show prototype apparatus for carrying out the method ofFIG. 1;

FIGS. 8 and 9 are simplified schematic diagrams showing suction devicesfor sucking insects into a release cartridge according to embodiments ofthe present invention;

FIGS. 10 and 11 show cooled insects on cage walls, in a state ready forimaging and classification according to embodiments of the presentinvention;

FIG. 12 is a chart showing the visual differences between male andfemale mosquitoes;

FIGS. 13 and 14 illustrate two actual images taken from a pupa tray withemerging adults for classification using the embodiments of the presentinvention;

FIG. 15 is a simplified flow chart showing a process for obtaining highand low resolution frames and carrying out detection, classification andtracking;

FIG. 16 is a view of a pupa tray with classification events according toembodiments of the present invention;

FIG. 17 illustrates a pupa tray with as yet unidentified regions ofinterest;

FIG. 18 illustrates a region of interest found in FIG. 17 according tothe present embodiments;

FIG. 19 shows a track made by an insect in the tray of FIG. 17 andtracked according to the present embodiments;

FIG. 20 shows the effects of piling up on the edges of a tray to preventclassification of individual insects in the pile;

FIGS. 21 to 23 illustrate pouring of cold insects onto a conveyor forclassification without piling according to embodiments of the presentinvention;

FIG. 24 is a simplified flow chart showing a procedure for pouringinsects onto a cold conveyor and classifying according to embodiments ofthe present invention;

FIG. 25 is a simplified flow chart showing an alternative procedure forplacing container walls with standing insects onto a cold conveyor andclassifying according to embodiments of the present invention;

FIGS. 26 and 27 show a schematic and a photograph respectively of arobot picker on a classification and sorting line according toembodiments of the present invention;

FIGS. 28 to 32 are schematic diagrams showing five different variationsor a robot picker according to the present embodiments, and

FIG. 33 is a simplified diagram showing how the insects in a fullcontainer according to the present embodiments may be fed.

DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

The present invention, in some embodiments thereof, relates to sexsorting of mosquitoes and, more particularly, but not exclusively, to amethod and apparatus for providing sorted male mosquitoes.

A method and apparatus for mechanical or electro-mechanical or otherautomatic sex-sorting of mosquitoes by extracting a class of mosquitoesfrom unsorted mosquitoes comprises obtaining unsorted mosquitoes,obtaining images of individual mosquitoes in a stationary phase,electronically classifying the individuals from the images into malemosquitoes and/or female mosquitoes, and possibly also unclassifiedobjects; obtaining co-ordinates of individuals of at least one of themale mosquito and female mosquito classifications, and using a robot armto reach an individual identified by the obtained coordinates to storeor remove the individuals, thereby to provide sex-sorted mosquitoes.

The stationary phase is any phase in which the mosquito does not move orbarely moves. One possibility is when the insects are cooled and remainmotionless on walls, or cooled further to fall to the floor. Anotherpossibility is when the adult insects emerge from the pupa.

Before explaining at least one embodiment of the invention in detail, itis to be understood that the invention is not necessarily limited in itsapplication to the details of construction and the arrangement of thecomponents and/or methods set forth in the following description and/orillustrated in the drawings and/or the Examples. The invention iscapable of other embodiments or of being practiced or carried out invarious ways.

Referring now to the drawings, FIG. 1 illustrates a flow chart showing ageneralized embodiment of the present invention. A method for mechanicalsex-sorting of mosquitoes by extracting a class of mosquitoes fromunsorted mosquitoes may begin with unsorted mosquitoes 10. A stationaryphase is identified, say during emergence as an instar, or applied bycooling 12, and the mosquitoes are imaged in the stationary phase—14.The images are used 16 to classify the individuals. Classes used mayinclude males and anything else, or females and anything else, or males,females and anything else. The coordinates are provided 18 alongside thegender of individuals of interest. In some cases the males are gathered,and in other cases the females are removed.

Then, a robot arm may be used to reach an individual identified by theobtained coordinates to store or remove said individuals, thereby toprovide sex-sorted mosquitoes 22.

Classifying may be carried out using a trained neural network, and theneural network may be a classical neural network or a deep network,including a convolutional neural network, or any other kind of neuralnetwork.

The trained neural network may comprise four or more layers.

In an embodiment, the stage of obtaining images 14 may relate toobtaining and considering single frames. Alternatively, successiveframes may be classified together by generating differences betweensuccessive frames and using the differences to determine whichindividuals are in the stationary phase. Thus if successive frames showa mosquito shape and little change, the implication is that a stationarymosquito is being viewed.

Successive frame classifying may involve using a recurrent neuralnetwork (RNN), as will be discussed in greater detail below.

In an embodiment, the unsorted insects are emerging pupae and thestationary phase at 12 is the emergence phase in which the adult emergesfrom the pupa.

Alternatively, the unsorted insects are adults and the stationary phaseis obtained by cooling the insects.

The insects are not necessarily absolutely stationary during thestationary phase, and thus an embodiment uses imaging to track 24movement of individual insects to update the coordinates obtained in box18, so that the robot arm moves to the correct location.

Obtaining images for classification may use a first, relatively highresolution, camera, and tracking may use a second, relatively lowresolution, camera with a wide field of view. Alternatively a singlecamera may be used for both, provided that it combines sufficientresolution with sufficient field of view.

In embodiments, the obtained coordinates are of the male class and theidentified individuals are picked off and placed in storage. The robotarm may use a suction device or a blower device to pick off theidentified individuals and place them in storage.

Alternatively, the obtained coordinates are of the female class and theidentified individuals are destroyed or otherwise disposed of. A smallnumber may be retained for breeding. The robot arm may comprise a zapperfor destroying the identified individuals, or may as before use asuction device or blower.

The zapper may be an electrode or a solenoid or a laser etc.

In an embodiment, if an individual is not classified into male or femaleby a predetermined time, then the image is sent to an operator foradjudication. Alternatively, if an individual is not classified as maleby a predetermined time then it is classified as female.

In an embodiment, the insects are cooled for imaging in a containerhaving walls, so that the cooled insects stand on an interior side ofthe walls. Then the box is dismantled to present the interior sides ofthe walls for imaging.

Referring now to FIG. 2, the insects are cooled in a container 30 havinga trap door and the trap door is opened onto a first moving conveyor 32,also held at low temperature, to allow the cooled insects to fall ontothe conveyor. The first moving conveyor carries the insects to animaging location 34 under camera-imaging system 35. The conveyor maystop when insects reach the imaging location to allow for imaging withcoordinates, and then robot 36 picks the selected insects, in this casegenerally the females, leaving the males to fall off the end of theconveyor into release boxes 38.

In an embodiment, the part 40 of conveyor 32 beyond the imaging area maybe a second conveyor which collects mosquitoes from the first conveyorand then travels more slowly to the filling area.

The first moving conveyor 32 may be a relatively fast moving conveyor,where the speed spreads out the falling mosquitoes, so that the insectsdon't pile and disrupt imaging. The first moving conveyor 32 may emptyonto second moving conveyor 40, being a relatively slow moving conveyor.

In greater detail, the present embodiments provide technology forautomatic or semi-automatic detection and classification of mosquitoes.

In order to be able to perform a good classification and selection ofthe mosquitos, the identified mosquito needs to be in a position whereit can be identified as an individual and seen clearly, and then itshould stay relatively still and certainly not fly away betweenclassification and the time at which it can be picked or removed in thesorting process.

Two different embodiments provide these properties in different ways.

A first embodiment uses cooling to cool down the air, so that themosquitoes do not move and then it makes the classification. Inembodiments, the temperature is lowered to ˜6-12 degree Celsius, lowenough so that the mosquitoes do not fly, but high enough so theycontinue standing, and not falling on the floor and getting tangled withone another.

A variation of the same embodiment lowers the temperature further, orthe cage is shaken with the previous level of cooling, so as knock themosquitoes down on the floor.

A second embodiment uses a system that monitors a tray of pupae ataround the stage of emergence, and utilizes the fact that the mosquitosex can already be visually identified just before the adult mosquito isfully emerged. At this point the mosquito is stationary or almoststationary for several minutes, providing sufficient time to detect,classify and then carry out selection. Such a system is made of a pupatray with water, pupa and a vision system. As will be discussed,different methods of image classification are suitable for differentembodiments, and these include both classical neural networks andconvolutional and other networks involving deep learning.

The present embodiments, may automatically sort and handle the adultinsects based on the classification process to provide sorted insects inrelease cartridges.

The present embodiments may either handle the males for selection andfurther use, leaving the females behind, or handle the females andremove them, leaving the males for further use.

The robotic system may potentially receive a priority list from thedetection and classification system, instructing it which mosquito tohandle first, as will be described in greater detail below.

An embodiment may include a pupa tray with water and pupa inside.

There may be further provided a vision sensor to detect and classify themosquitoes upon their emergence.

Optionally a cage with nets surrounding the pupa tray in order toprevent any emerging mosquitoes from escaping to the outside.

A robotic system may contain an arm and a handling tool for handling theindividual mosquitoes. The robotic system also includes a controller toreceive coordinates and guide the robotic arm and handling tool to moveto those coordinates. The movements may be on X-Y-Z axis, providing theability for the handling tool to reach all areas in front of the surfaceon which the adult mosquitoes are standing.

In one embodiment, the handling tool may comprise a suction tube and amotor with controller. Suction may be in the order of 3-8 meters persecond for a tube diameter in the order of 6-12 mm. Larger tubes arepossible but may suck more than just the selected target mosquito, thusupsetting the selection ability of the system.

The handling tool travels to the mosquito X-Y position, and the suctiontube is lowered along the Z axis to meet the mosquito.

Reference is now made to FIGS. 3 and 4, which illustrate a prototype inwhich a pupa tray with water is located below a framework holding arobot arm.

As shown in FIGS. 3 and 4, a proximity sensor 50 may be located abovepupa tray 52 to sense the distance to the water surface as a safetymeasure. The same or another sensor may follow the suction tube 54 whichis being operated by robot arm 56 to ensure it reaches a distance ofabout 0.5 cm from the mosquito, or about 1 cm from the water surface.That is to say the sensor provides feedback on the suction tube positionfor correct actuation.

Reference is now made to FIG. 6, which is a schematic diagram showingsuction tube 62 and motor-controller 64. The tube moves over the area oftray 66. Once the suction tube is above the mosquito coordinates,suction operates to pick off the selected mosquito which travels on to astorage cartridge 58 such as that shown in FIG. 5. The mosquito 60 thenenters the cartridge.

In that way, the emerging male mosquitoes are selected and placed intothe release cartridges.

For colony maintenance, selected females and males can be transferredand puffed together into rearing cages, to keep the size of the rearingcolony. Females can also be eliminated through being transferred intofemale cages for elimination if needed.

As the mosquitoes are sucked one by one, there may be continuousmonitoring of the number of mosquitoes that are loaded into each releasecartridge, or into the rearing cages. FIG. 7 illustrates a releasecartridge 70 that is full.

In another embodiment, instead of suction of the female mosquitoes, alaser module that can be provided with coordinates, can shoot a laserbeam and kill a female mosquito. In that embodiment, the laser beam isdirected towards the mosquito female with coordinates provided by thedetection and classification process.

The term “zap” is used herein to describe different ways of killing thefemale mosquitoes, including such use of a laser beam.

In another embodiment, instead of sucking the males, it is possible towait until they fly. In that case there are two options:

In the first option, the tray of pupa is placed inside a cage.

The females are extracted or zapped a robotic guided zapper or by alaser beam or other type of zapper, for example an electrode thatelectrocutes the insects, or a solenoid which, when trigged, pushes thefemale mosquito into the water to drown.

The males may then be left to emerge into the cage.

By the end of the process the idea is that there is a pupa tray with nopupa, and a cage with males only.

The robotic zapper is either placed inside the cage, requiring a largeenough cage, or the zapper can enter through an opening each time thevision sensor classifies a mosquito as a female mosquito.

The vision system is placed at a distance that provides the requiredresolution for the sensor, be it just above the pupa dish, or above thecage with a transparent roof top and zoom-in capability.

Typical camera resolution may be 5 MegaPixels, that is using a sensor of2000×2500 pixels. If the field of view is a region having a length of 30cm, you divide by the dimension and get the number of pixels per mm. Thepresent embodiments may have at least two to three pixels along thelength of the distinguishing features of the mosquitoes. The distance ofthe camera from the tray and the number of pixels on the sensor are thusconnected to provide a sensitivity.

Reference is now made to FIG. 8, which illustrates a second option.Above the pupa tray 80 there is a tube 82 for guiding the emergingmosquitoes towards release cartridges. In that way, many mosquitoesemerging from a single pupa tray may be guided through ducts towards arelease cartridge. Once the system identifies that the number ofmosquitoes transferred into that cartridge has reached the requirednumber, the mechanism may be switched to start filling the nextcartridge. This can happen for example by moving away the filledcartridge and bringing in below the outlet of the main duct a newrelease cartridge to be filled.

In order for the cartridges to be actively guided, air flow may beintroduced into the duct, to support moving the mosquitoes along theducts, toward a main duct and then to the release cartridge.

Referring now to FIG. 9, the suction unit 90 may be placed above thecorrect coordinates of the identified insect in the tray 92 as providedby the camera (not shown) after running the detection and classificationprocess. A guiding duct 93 with airflow leads to release cartridge 94.

The release cartridge 94 has an opening wherein upon pushing from theoutside the guiding duct 93 can enter into the release cartridge withoutletting the mosquitoes escape to the outside.

In prototypes, the emergence process was shown to take about 3-5minutes, and that after the mosquitoes is fully emerged with all of its6 legs, it will usually stand still on the water for at least 3 moreminutes. Some walked after 3 minutes, some walked or flew after 15minutes and more.

The detection and classification per each frame was shown to last about0.5-1.5 seconds, with an average of 1 second.

The distance the robotic zapper needed to move in order to reach thecoordinates of the mosquito was in the order of dozens of centimeters.The time required for that is in the order of 2 seconds, including thesuction. The classification process may continue in parallel to therobotic operation, hence, while the robotic arm is moving towards amosquito, the algorithm already classifies the next one, and so thetotal cycle between mosquitoes is 2 seconds. This means that over 24hours, a robot can sort around 43,000 mosquitoes. It may be decided touse fresh plates every 24 hours to increase productivity. Hence for adaily sorting production rate of 5M mosquitoes (or 35M per week), some116 robotic systems are needed.

Today, the cost of a single robot at that size and reach is in the orderof 5,000 Euro, resulting in a total cost of around 500K euro for theautomatic sorting equipment for a facility with a production rate of 35millions mosquitoes per week. This is much more affordable than thehundreds of personal that would be required in order to achieve the samerate manually.

In the second main embodiment as presented in the beginning the systemincludes a cooling unit, able to lower the temperature to 6-12 Celsiuswith 1 degree Celsius resolution, a cage with unsorted adult mosquitoes,a robotic system comprising a zapper and a mechanism for travellingalong the sides and top-bottom of the mosquito cage, preferably from theexternal side of the cage.

The method comprises cooling the temperature and waiting five minutesuntil the mosquitoes have ceased flying but are rather resting on thecage walls.

Detection and classification is carried out on the mosquitoes, and forindividual mosquitoes, the gender is output. If the robot is neededbecause the classification indicates a mosquito that needs to becollected or zapped, then an output is provided giving the coordinateson the walls and gender.

The vision sensor, typically a camera, is placed alongside the cage tocover all walls. Alternatively, multiple cameras may be used.

Once a female mosquito is classified, a laser beam kills the mosquito atthe female mosquito coordinates, or alternatively, a suction tube isbrought to the coordinates to suck the female mosquito through the net.The suction in that case would be higher than in the previous embodimentwhere the targets are the adults emerging from the pupa stage. A typicalspeed may be in the range of 20 m/s in order to be able to get over themosquito's efforts to cling to the cage walls. In another embodiment thesuction unit zapper may be placed inside the cage.

Referring now to FIGS. 10 and 11, the mosquitoes 100 are in enclosures102 which are cooled to around 8 degrees. The mosquitoes appear clingingto the box wall after the temperature is lowered. Distinct genderfeatures can be easily seen and mosquitoes can be classified. FIG. 12shows male and female mosquitoes side by side and shows the visualdifferences between the two. Specifically, in the male the antenna isbushy, the maxillary palp is longer and the proboscis is also longer. Inthe female the antenna is smooth, and the maxillary palp and proboscisare both shorter.

The following relates to a method of automatically detecting andclassifying mosquitoes as males and females, and is described in thecase of emergence from a pupa, but also applies to imaging under coldconditions.

For a semi-automatic version, the system may signal an operator on itsfinding for a decision.

As well as a classification, the system may provide the coordinates ofthe classified mosquito to the robotic controller to work according toany of the options mentioned herein, such as to guide a suction tube andsuck the mosquito to transfer it to a cartridge, kill it with laserbeam, etc.

The concept is primarily for mosquitoes but may apply to otherapplications in which insect rearing is involved, sex sorting isrequired and there is a distinguished visible patterns that can be thetarget of computer vision algorithms in a similar way to the methodssuggested in this application.

In the following we describe the present embodiments with reference tothe version in which the mosquitoes are emerging, but the same detailsapply mutatis mutandis to lowering the temperature.

The same algorithm and method apply to detecting and classifying undercold conditions, but there are differences. In cold conditions [1] Thereis no emergence process that has to be captured, [2] there is noprioritization, since there is no emergence, [3] the background has lessvisual noise (the pupa shell, the water surface), which improves thegeneral performance (speed to reach a positive result).

When the mosquito orientation is such as in FIG. 12, those features areeasily noticeable as long as there is a line of sight between themosquito and a direct vision sensor located above.

As suggested, achieving this can be when either the temperature is low,the mosquitoes cling to the walls or, during the emergence process.

The emergence process takes a few minutes (around 3-5 minutes withpossible exceptions) until the mosquito emerges out of its pupa, andthen it may continue standing on the water. That is to say the emergingmosquito may walk slightly but usually not much. Generally the emergingmosquito remains still for an additional few more minutes to harden anddry its exoskeleton. During all that time, as it almost does not move,its orientation is the same with reference to a vision system observingit from above.

FIGS. 13 and 14 are actual images of insects emerging from pupae in atray full of pupae. FIG. 13 shows a male and FIG. 14 shows a female.

The outputs are the coordinates of the classified mosquitoes, providedfor the operator or a robot controller.

The system may comprise a tray of pupae. As in the second embodimentdescribed above, there is a cooling unit for cooling down to 6-12 degreeCelsius, and there may also be provided a surface for knocked downmosquitoes.

A vision system such as a camera, controller unit and software, mayacquire a sequence of continuous frames of the mosquito in order todetect classify and/or track its position.

Since the mosquito can be oriented in different ways, with some visualnoise in the background, embodiments may use a deep learning approachbased on convolutional neural networks to detect and classify themosquito sex.

In another embodiment, pre-processing of the image may be carried outusing methods from classical image processing, such as obtainingoutlines etc. Then the deep learning is provided with the processedresults.

The vision module may processes each of a plurality of frames takenseparately and independently. Alternatively the succeeding frames can beused together to make available a sequential form of the problem, andtreat the input as a video. However it is generally believed that forvideo processing, wrapping a single image processing module in a RNNarchitecture may gain better results.

The present embodiments may contain one or two cameras above the tray ofpupas in which tray it is expected that the adults are about to emergeany moment, A robot arm with a laser, or any other zapper may also beprovided that may operate at the given mosquito coordinates according toa set of algorithms.

The set of algorithms may find where, in the image and in the physicalworld, there is an emergence of a mosquito from a pupa and may thentrack its location with time.

A high resolution camera, may look for the features of the mosquito thatparticularly identify the gender, such as the antenna area. In additionthere may be a low resolution camera that is required for tracking.Alternatively, a single camera may provide both detection-classificationand tracking, if the tracking area is covered within the camera field ofview (FOV).

The inputs for imaging and location are: a continuous sequence of imagesfrom an upper projection of a tray with mosquitoes and pupae in waterinside the tray.

A requirement may be:

“Finding the real world coordinates of all female mosquitoes in thecaptured tray at any given moment”.

In that case the object will be classified either as female, or “other”(either male or pupa or other visual noise). This can be useful forquality control or when it is only needed to extract the females. It ispossible to use the algorithm breakdown below and define a differentpurpose—“Finding the real world coordinates of all male mosquitoes inthe captured tray at any given moment”, or further to “find the realworld coordinates of all male mosquitoes and female mosquitoes in thecaptured tray at any given moment”.

For that purpose, we may use the following features:

1) Camera calibration (one time for system set-up, and as necessaryduring maintenance etc.)

2) Detect ROI's (region of interest) of each mosquito.

3) Classify gender of each mosquito

4) Prioritize mosquitoes

5) Track each classified female mosquito

6) Transform pixel coordinates system into real-world coordinates system

Reference is now made to FIG. 15 which is a block diagram describing theimaging and location flow, with the assumption that two cameras areused; one for detection-classification (aka High Resolution Camera) andthe other with larger FOV for tracking (aka Low Resolution Camera).

In box 110 a high resolution frame is obtained. In box 112, detectionand classification is carried out. In box 114, coordinates are taken andtransformed for the camera and robot frames of reference. In box 116 alow resolution frame is obtained, and in box 118 tracking is carried outto update the coordinates.

The layout can be duplicated to match a layout with more trays orcamera. Tracking can be mutual to a few camera looking each on part of atray.

Upon system set-up there is a camera calibration procedure in order toenable finding the transformation between the pixel's coordinates systemand the physical coordinate system.

If the system consists of two cameras, e.g. high & low resolution assuggested above, then a calibration between each camera may be derivedfrom the calibration parameters of each camera.

Camera calibration using a check-board or other patterns is known, andsuch known methods are provided as an example:

“A Flexible New Technique for Camera Calibration”, Zhengyou Zhang, 1998,Microsoft(www(dot)microsoft(dot)com/en-us/research/publication/a-flexible-new-technique-for-camera-calibration/).

“A Four-step Camera Calibration Procedure with Implicit ImageCorrection”, Janne Heikkilä and Olli Silvén, University of Oulu,Finland.

The implementation of the algorithm is common knowledge, and sample codeis publicly available.

In camera calibration there is a model that is divided into extrinsic(orientation and location) and intrinsic (mainly optical) parameters.Having all of these parameters allows a transformation between the imagecoordinates system and the real-world coordinate system.

Calibration is done by capturing several pictures in variousorientations of a checkerboard or other easy to detect pattern. Bycorresponding detection or marker points in each of the pictures, anoptimization process may be converted to the correct camera modelparameters.

Once we have the transformation parameters (intrinsic and extrinsicparameters) we can translate mosquito location to a physical locationfor the mechanical system to work.

Since the cameras are mechanically stable, the calibration process isdone once (or for maintenance reasons every couple of months\years).

With two cameras, and having the transformation parameters for eachcamera we calculate the transformation between the two cameras' pixelcoordinate systems. Then we can use coordinates from the high resolutioncamera and transform them into the low resolution camera for thetracking algorithm, and output them either for the mechanical zapper orpinpointing for a human operator where the classified mosquito is to befound.

An accuracy of the calibration is a system parameter that is used forthe tracking algorithm and for the mechanical cannon.

The detection (the task of finding where the mosquitoes are in theimage) and the classification (the task of determining which gender itis) may be jointly solved by any of the following algorithms asexamples:

Single Shot MultiBox Detector (https://arxiv(dot)org/abs/1512.02325Authors: Wei Liu1, Dragomir Anguelov2, Dumitru Erhan3, ChristianSzegedy3, Scott Reed4, Cheng-Yang Fu1, Alexander C. Berg1, UNC ChapelHill 2Zoox Inc. 3Google Inc. 4University of Michigan, Ann-Arbor).

Faster rcnn: www(dot)arxiv(dot)org/abs/1506.01497 (“Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks”,authors: Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun).

Yolo: www(dot)pjreddie(dot)com/media/files/papers/yolo(dot)pdf.

These kinds of algorithms may train a network that is a combination ofan RPN (region proposal network) and a classification network thatshares the same convolutional features. The RPN proposes variousbounding boxes that contain an object with high probability and this wayactually tells the classification network where to look in the image.The classification network may be trained to determine to which classthe object belongs.

In the present embodiments, concerning detection-classification ofmosquitoes, we may define the possible classes as male\female\none oronly as female\none or only as male\none In order to train the networkwe may collect a large number of labeled pictures containing male andfemale mosquitos.

To train for only one class, female for example, we may provide thenetwork with either male pictures or background pictures for thenon-female class. This way the network may train to look for therelevant attributes of females and not general attributes of mosquitosthat are common to males and females.

The background pictures may be empty water or pupas or mosquitoes whosegender cannot be determined yet.

The classification net in each of these algorithms may be changed and wemay use transfer learning as fine tuning or as a feature vector, whichmay be done using nets such as Alexnet, VGG and Inception.

Transfer learning is described in those examples: “ImageNetClassification with Deep Convolutional Neural Networks”, AlexKrizhevsky, Ilya Sutskever, Geoffrey E. Hinton(www(dot)papers(dot)nips(dot)cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks(dot)pdf).

“VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION”Karen Simonyan & Andrew Zisserman(www(dot)arxiv(dot)org/pdf/1409.1556v6(dot)pdf).

The output of the detection & classification for a single image is alist of rectangles (ROI's) and corresponding probabilities as to whethereach of them is a female mosquito, or a general probability that theobject belong to each of the classes. This output is a list of vectors

(X_i,Y_i,W_i,H_i,[P_i_1, . . . P_i_n])

where: I is ROI detected index and n is the number of classes:

(X_i,Y_i)=are coordinates in the image of where the detected ROI is

(W_i,H_i)=are the width and height of the detected ROI, and

(P_i_1,P_i_2, . . . P_i_n) is the list of probabilities of the object inthe ROI to belong to each of the classes.

Reference is now made to FIG. 16, which shows a tray of pupae andemerging adults. Results are shown of a detection & classificationalgorithm for two classes and 5 detected insects. It is to be noted thateach class is given a probability.

A possible network configuration may be an RNN (recurrent neuralnetwork) which is a network that store state and classify differentlyaccording to its current state.

The architecture we propose works on a single image. It is known that invideo processing there is an advantage to using RNN architecture. Inthis way the continuity of the images taken before the mosquito fullyappears affects the probability that it will be male or female. Theaccumulated additional knowledge from each frame increases theprobability for positive classification.

Relevant methods:

www(dot)cv-foundation(dot)org/openaccess/content_cvpr_2015/papers/Ng_Beyond_Short_Snippets_2015_CVPR_paper(dot)pdf.www(dot)static(dot)googleusercontent(dot)com/media/research.google(dot)com/en//pubs/archive/42455(dot)pdf.

A further implementation also uses previous frames to process a currentframe and thus comes under the heading of using video as input.

In this implementation, since the camera is stable, and the pupas hardlymoving, only areas of emergence change over adjacent frames. Thus we areable to detect the moment of emergence to be utilized for prioritizationas will be discussed in greater detail below. Such knowledge can be usedby adding a term to the loss function of the RPN that describes a deltabetween successive image frames, thus the L2 measure of images or offeature space. In other words the embodiment punishes results ofdetection where there is little variation and incentivize the RPN fordetection results where there is high variation.

During the emergence phase there arises a point in time when themosquito stops changing its form, which point may be confirmed againstan average or maximum time from beginning to end of emergence, or fromthe moment either six legs of the mosquitoes are out of the pupa.

One method is to determine the time that the image ROI begins to changemore rapidly, say using a threshold on adjacent image L2 deltas. Theapproach is feasible since the pupa images before the emergence processbegins are stable, older pupa reaching the stage of emergence hardlymove in water. The pupa trays may already be sorted and tagged accordingto date of pupation (transformation from larva to pupa).

A second method is to train the classification network to classifybetween more than two classes, namely male and female, and instead usefour or more classes: young male, adult male, young female, and adultfemale. The young male or female classes are defined as those mosquitoeswhose legs are not yet fully seen and adult males or females are thosemosquitoes whose body is fully exposed. Using this method the number ofclasses can be extended to more than four for a finer distinguishing ofdifferent levels of emergence.

The system may store in memory all instances of emerged mosquitoes andtheir timing (according to above rule), and then provide the nextmosquito to be handled according to a FIFO queue (first to emerge, firstto be handled).

The training process of the neural networks is a process that may becarried out once using a large labeled database. There are many factorsthat affect good training: selecting activation functions, selectingoptimization model, how to initialize the weights of the net, determinehyper-parameters, dropout, data augmentation and many more.

The result of a good training process is a set of final values of theweights of the neural networks, which can then be used to classify theimages.

Using a trained network on the training database may give the time sincethe start of emergence that is required to arrive at a successfulclassification.

For example, the average number of frames (given a constant frame rate)from the start of emergence until the gender classification may be knownwith 90% probability to be 250 frames (at a rate of 2 frames persecond). This information, which may be collected during the trainingprocess, may serve the robotic system afterwards to know how much timeremains to operate on the classified mosquito.

FIG. 17 is a typical input image where lots of pupae are seen and aregion of interest needs to be looked for.

FIG. 18 show a region of interest that has been found in FIG. 17 andcontains an adult mosquito needing classification.

Tracking may be carried out using a lower resolution camera, ifprovided.

Once fully emerged, a mosquito may travel on the surface and tracking issuggested to provide correct coordinates for the operator/robotichandling tool.

Tracking algorithms exploit information of a movement model thatcontains common velocities and accelerations, to punish\incentivizepossible locations of the target in the next frame. Using tracking,partial occlusion or error on the target detection can be compensated.

Tracking parameters for the tracking algorithm may include:

Average/maximal velocity of mosquitos;

Average/maximal acceleration of mosquitoes;

Average/maximal of movement duration;

Angular velocity of movement;

Angular acceleration of movement;

Camera parameters, such as focal length, distance of camera from tray,camera orientation) for translate all spatial units to pixel units; and

Camera exposure time to avoid blurring of a moving object.

An existing tracking algorithms that may be used is a Kalman filterbased tracking algorithm.

FIG. 19 shows an insect 117 and a track 119 that the insect 117 hasfollowed.

A pupa tray such as in FIGS. 17 to 19, contains pupae and water. Avision sensor may capture frames from the tray and a deep learningalgorithm uses the frame to detect-classify an emerging mosquito. Thenthe tracking algorithm tracks the mosquito if it moves. Then optionallythe coordinates may be sent to an operator in the semi-automaticprocess, or tracked coordinates may be sent to a robotic system to carryout suction of the mosquito or killing the mosquito with a laser beam orother means.

An alternative embodiment may work on insects as they are warmed andslowly become active. The embodiment may involve cooling of the airwhere the mosquitoes are stored to a temperature of 6-12 degrees, sothat the mosquitoes are resting, almost not moving, but not falling onthe ground. Alternatively, the temperature may be set to lower than 6degrees, so that the mosquitoes are motionless on the floor.

The vision sensor then operates on the cage or plate with the cold andmotionless mosquitoes. As in the previous embodiment, a vision algorithmwhich may be based on deep learning may detect-classify the mosquitoes.If the temperature is above six degrees then tracking may be needed asthe mosquitoes do move somewhat. At temperatures below six degrees thereis no motion and tracking is not needed.

If training only one class (female for example) one may provide thenetwork either male pictures or background pictures to the non-femaleclass. This way the network may train to look for the relevantattributes of females and not general attributes of mosquitos that arecommon to males and females. This may create a more efficient network.

An embodiment may run two networks in parallel on the same frames—onethat classifies only females, and a second that classifies only males.

As mentioned above, instead of working on individual frames, using videomay have an advantage.

If using video then a possible network configuration is RNN (recurrentneural network) which is a network that stores a state and classifiesdifferently according to its current state. The continuity of the imagestaken before the mosquito fully appears affects the probability that itwill be male or female. The accumulated additional knowledge from eachframe increases the probability for positive classification.

The system may be able to identify, that is detect the emerging mosquitoand where it lies in the emergence process, that is the system mayidentify the point in time when a mosquito is first fully emerged.Metrics that may be used include average time from start of emergence.Alternatively visual classification of parts of the emergence processmay be used. The results are used to enter the current emerging mosquitointo a queue for the robotic system, which the robotic system may thendeal with in order.

For scaling the detecting and sorting process to large quantities, anautomated system may continuously feed the vision system withmosquitoes, however the mosquitoes should be in a state in which theycan be clearly imaged. Thus the mosquitoes should be separated from eachother, or at least not one on top of the other, so that vision system isable to identify the unique gender features of individual mosquitoes andprovide an answer as to whether the object is a mosquito and then if itis a male or female mosquito.

However, in order to increase the yield of the system, large numbers ofmosquitoes may pass through the vision system per unit time.

However, if one takes a storage compartment with a large number ofmosquitoes and simply decreases the temperature until the mosquitoesfall motionless, then one simply forms piles of indistinguishableindividuals. FIG. 20 shows a plate of Asian Tiger mosquitoes, where thetemperature is reduced below 8 degree Celsius and the mosquitoes haveformed piles in which individuals cannot be classified.

Here we easily notice a situation in which for the middle area 122,where mosquitoes are not one on top of the other, it is easy to identifythe mosquitoes, and also their sex, however at the edges, exemplified by120, we see bundles of mosquitoes for which it is not possible toidentify the single mosquitoes and their sex.

In the following we provide two embodiments for a continuous feed ofmosquitoes without piling one on top of the other.

A first embodiment comprises:

-   -   a. A cooling system;    -   b. Mosquito storage compartments;    -   c. Conveyance mechanism to move mosquitoes forward;    -   d. Vision system with controller. The controller may be        connected to the conveyor to stop when required and to provide        coordinates;    -   e. Pick and place robot to suck or blow. For example it may        include a common suction pipette or electric air pump or a        blower which can be used either for blowing or in reverse as a        suction device. The robot may puff male or female mosquitoes.        Alternatively a zapper such as a laser beam may puncture or        extract female mosquitoes in any other way which kills them.

In the embodiment the temperature is lowered so that the mosquitoes fallonto the floor below. However the floor is moving, since the floor is amoving conveyor, and thus no piling up occurs.

FIG. 21 illustrates mosquitoes being dropped from container 130 onto amoving conveyer 132, where the motion of the conveyor is used to ensuremosquitoes are spread out and not piled up. On the right side is asensor 134 to identify the mosquitoes from above.

A variation is to cool the mosquitoes so that they fall in a pile, andthen open a trap door underneath onto a moving conveyor. This is shownin FIGS. 22 and 23. FIG. 22 shows belt 140 without any mosquitoes andFIG. 23 shows the belt with mosquitoes falling on after trap door 142has been opened.

In FIG. 23 the storage trap doors are opened, and mosquitoes fall whilethe conveyor is moving, and as seen, mosquitoes are separated on theconveyor.

A procedure is shown in the flow chart of FIG. 24.

-   -   a. Mosquito storage compartments are held at a low temperature        as mentioned above—b 150.    -   b. Mosquitoes inside the storage compartments are now knocked        down—152.    -   c. Mosquitoes may then be transferred to the conveyor—154, by        pouring them directly on the conveyor. Transfer may be carried        out while the conveyor is moving in order to ensure separation.        Transfer happens by pouring the mosquitoes directly from the        storage or using another item to transfer them from the storage        to the conveyor. For example a suction tube may suck the        mosquitoes from the storage floor onto the conveyor surface.    -   d. Once most or all of the content of a storage compartment is        transferred onto the conveyor, another storage compartment (or        set of compartments, in order to work in parallel) 155 is        brought in such proximity to the conveyor to repeat the process.        2. The conveyor may move the mosquitoes under cold conditions as        mentioned above towards a robotic handling point. In        embodiments, two conveyors may be used. In such a case, the        first conveyor may have been used to separate the mosquitoes,        and at the end of the first conveyor the mosquitoes may then        fall onto the main conveyor that brings the mosquitoes to the        robotic inspection and sorting station. In such an example, the        two different conveyors may also move at different speeds. A        higher speed may be useful to separate the mosquitoes, which        then fall onto the secondary conveyor, to move slowly towards        the robotic station.        3. At an inspection point, a top camera detects and classifies        the mosquitoes according to sex 158 as explained above. The        conveyor controller may command the conveyor to stop moving 156        for a short period, say a few seconds, until the vision system        algorithm completes the detection-classification process for the        objects in the field of view. The conveyor moves again, and once        the field of view is again full with as yet unclassified        mosquitoes, the conveyor stops again. Alternatively, the        detection-classification process may happen at the same position        at which the picking robot is located as will be described        below. In this alternative, the conveyor stops, the picking        robot picks the classified mosquitoes, and then the conveyor        moves again a pre-defined distance to enable another set of        mosquitoes to be located both under the vision and the picking        robot reaching area.        4. Receiving the coordinates and performing tracking of the        mosquitoes may be carried out by one or two cameras.        5. A picking robot, for example a delta pick and place robot,        receives the coordinates of the mosquito picks 160. Picking may        be as follows:    -   a. The picking robot holds a suction source;    -   b. The suction source sucks the mosquitoes approaching from the        conveyor;        -   i. either into a temporary suction tube, which are then            expelled into a release storage unit or cartridge when full,            that is when they receive a pre-defined number of sucked            mosquitoes;        -   ii. Or after each suction the mosquito is expelled directly            into a release storage unit that is located close to the            suction robot, preferably also under cold conditions.    -   c. The robot may count 162 the number of mosquitoes sucked and        expelled into release boxes;    -   d. Once the release storage unit has reached a certain number of        mosquitoes the release storage box is exchanged for a new one        164. The change may be manual or automatic.        A second embodiment comprises    -   a. A cooling system; and    -   b. Mosquito storage compartments having walls to which        mosquitoes may cling. The storage compartments may be        de-attached or folded out, so that instead of a box, after        de-attaching the walls from each other, the result is a set of        panels to which the mosquitoes cling. The walls may comprise        aluminum nets with small holes.    -    The mosquitoes may thus be presented resting on nets without        flying, and not being one on top of the other.    -   c. A conveyor or rail system may move the panels with mosquitoes        forward;    -   d. Vision system with controller as discussed above. The        controller may be connected to the conveyor to stop the conveyor        when required so as to provide the pick and place robot as        discussed above with the necessary coordinates;    -   e. The pick and place robot as discussed hereinabove

The procedure is now discussed with reference to FIG. 25 which is a flowchart illustrating the present embodiment.

Initially the temperature is lowered 170 using the cooling system sothat the mosquitoes do not fly in the storage compartment, and also donot falling onto the storage floor. Such a temperature for the Asiantiger mosquito is for example usually below 13 degrees Celsius notflying, but higher than 4 degrees Celsius allowing the insects to clingto the walls—172. A preferred temperature is above 8 degree Celsius.

The cage walls on which the mosquitoes are resting are then taken out174 and attached to the moving conveyor or rail system.

The cage walls when placed on the moving element (conveyor or rail), areoriented so that the mosquitoes are resting on the top part which waspreviously the internal part of the storage compartment, and the visionsystem and also the pick and place robot unit may thus have directaccess to the mosquitoes.

The conveyor may propagate the walls forward, and with them, themosquitoes, under cold conditions as mentioned above towards the robothandling point.

At the robotic handling point, a top camera detects and classifiesmosquito sex—178, as discussed hereinabove. Tracking may be requiredsince the temperature is high enough to allow mosquitoes to walk on thewalls. The conveyor controller may command the conveyor to stop movingfor a short period 176, say a few seconds, until the vision systemalgorithm has completed the detection-classification process for theobjects in the field of view. Afterwards the conveyor may move again,and once the field of view is again full of unclassified mosquitoes theconveyor may stop again. Alternatively, the detection-classificationprocess may take place at the position where the picking robot islocated, and thus the conveyor may wait for the picking robot to pickthe mosquitoes of the appropriate class 180, and afterwards move apre-defined distance to enable another set of mosquitoes to be locatedboth under the vision and the picking robot reach area.

The various embodiments for picking the mosquitoes are the same as inprevious embodiments and are not repeated.

If the mosquitoes are already sorted so that there is only one sexinside the cages, then the embodiments may load the release boxes with apre-determined number of mosquitoes. The vision system in such a casewill only need to detect and classify a single class of mosquito,without the need to classify it further as a male or female. The robotmay then pick and place the mosquitoes using suction into the releaseboxes as described above.

In embodiments, if the vision system is unable to classify the mosquitoas a female or male, it may send the image to an operator, who may carryout the classification. Other embodiments may zap unidentifiedmosquitoes along with the females.

FIGS. 26 and 27 show a schematic and an example of a pick and placedelta robot 200 integrated into an automated sorting line 202 withconveyors 204 and 206, and camera 208. Camera may be connected to othercomponents 210 of a vision system.

In the present embodiments, the delta robot 200 has a suction unit, andthen it either sucks the mosquitoes directly lying on the belt and puffsthem into release boxes to fill the boxes, or the robot may suck themosquitoes from nets on which the mosquitoes are standing, as per someof the above embodiments.

Reference is now made to FIG. 28, which is a simplified diagram showingan embodiment in which the females are identified and zapped and themales are collected. A container with water and pupae 280 is provided,along with a camera 282 for imaging emerging adults for classificationand a laser 284 for zapping the females. Air flow from fan 286 blows thesurviving adults through duct 288 past camera 290 or other sensor whichcounts the passing mosquitoes. Container 292 collects the mosquitoes andis exchanged for a new container 294 when the count determines that itis full.

Reference is now made to FIG. 29, which is a simplified diagram of anembodiment that differs from the version in FIG. 28. Here the males areidentified and actively sucked up. Parts that are the same as in theprevious embodiment are given the same reference numerals and are notdescribed again except as needed for an understanding of the presentembodiment. A container with water and pupae 280 is provided, along witha camera 282 for imaging emerging adults for classification. A suctiontube 296 is provided on the end of a robot arm 298 to actively suck themales through duct 288. Container 292 collects the mosquitoes and isexchanged for a new container 294 when full.

Reference is now made to FIG. 30, which is a simplified diagram of anembodiment that differs from the version in FIG. 28. Here the males areleft to fly for collection, whereas the females are actively sucked intoa separate container. Parts that are the same as in the previousembodiment are given the same reference numerals and are not describedagain except as needed for an understanding of the present embodiment. Acontainer with water and pupae 280 is provided, along with a camera 282for imaging emerging adults for classification. A suction tube 296 isprovided on the end of a robot arm 298 to actively suck the females intocontainer 300. The males fly and get into the airstream driven by fan286 to be driven through duct 288 past camera 290 or other sensor whichcounts the passing mosquitoes. Container 292 collects the mosquitoes andis exchanged for a new container 294 when the count determines that itis full.

Reference is now made to FIG. 31, which is a simplified diagram of afurther embodiment of the present invention. As with FIG. 28, thefemales are zapped by a laser and the males are left to fly into anairstream. Parts that are the same as in the previous embodiment aregiven the same reference numerals and are not described again except asneeded for an understanding of the present embodiment. A container withwater and pupae 280 is provided, along with a camera 282 for imagingemerging adults for classification. Laser 284 is directed to zapfemales. The males are left to fly and get into the airstream driven byfan 286 to be driven through duct 288 past camera 290 or other sensorwhich counts the passing mosquitoes. Container 292 collects themosquitoes and is exchanged for a new container 294 when the countdetermines that it is full. Duct 288 and the containers are activelycooled to slow down the mosquitoes so that more can be packed into asingle container.

Reference is now made to FIG. 32, which is a simplified diagram of afurther embodiment of the present invention. In this embodiment, allemerging insects fly away from the pupa dish and are brought to aselection area where they are cooled and thus immobilized. Then themales and females are separated. Parts that are the same as in theprevious embodiment are given the same reference numerals and are notdescribed again except as needed for an understanding of the presentembodiment. A container with water and pupae 280 is provided, Allemerging adults are left to fly and get into the airstream driven by fan286 to be driven through duct 288 past camera 290 or other sensor whichcounts the passing mosquitoes to selecting surface 310 which is cooled.Above cooled selecting surface 310 is camera 282 for imaging emergingadults for classification. A suction tube 296 is provided on the end ofa robot arm 298 to actively suck the males through tube or duct 316 intocontainer 292. A second suction tube 312 is operated by second robot arm314 to suck the females into container 300. Container 292 collects themosquitoes and is exchanged for a new container 294 when the countdetermines that it is full. The count from second robot arm 314 may besubtracted from the count at camera 290 to give the number of malesbeing introduced into container 292. Duct 288 and the containers mayalso be actively cooled as before.

It will be appreciated that any of the previous embodiments may be usedto carry out the sorting, and females may be zapped or left in place asmales are removed etc.

Reference is now made to FIG. 33, which is a simplified diagram showinghow the insects may be fed when in the containers. The sorting andfilling station 320 provides containers 322 full of mosquitoes. The fullcontainers are transferred for storage, and have feeding holes or afeeding net on one surface 324. Feeding robot 326 spreads sugar water orthe like on the surface 324 to feed the insects. It is expected thatduring the life of a patent maturing from this application many relevantrobot picking, vision and learning technologies will be developed andthe scopes of the corresponding terms are intended to include all suchnew technologies a priori.

The terms “comprises”, “comprising”, “includes”, “including”, “having”and their conjugates mean “including but not limited to”.

The term “consisting of” means “including and limited to”.

As used herein, the singular form “a”, “an” and “the” include pluralreferences unless the context clearly dictates otherwise. For example,the term “a compound” or “at least one compound” may include a pluralityof compounds, including mixtures thereof.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable subcombination or as suitable in any other describedembodiment of the invention. Certain features described in the contextof various embodiments are not to be considered essential features ofthose embodiments, unless the embodiment is inoperative without thoseelements.

Although the invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims.

All publications, patents and patent applications mentioned in thisspecification are herein incorporated in their entirety by referenceinto the specification, to the same extent as if each individualpublication, patent or patent application was specifically andindividually indicated to be incorporated herein by reference. Inaddition, citation or identification of any reference in thisapplication shall not be construed as an admission that such referenceis available as prior art to the present invention. To the extent thatsection headings are used, they should not be construed as necessarilylimiting.

1. Method for mechanical sex-sorting of mosquitoes by extracting a classof mosquitoes from unsorted mosquitoes, the method comprising: obtainingsaid unsorted mosquitoes; identifying individuals in a stationary phase,the stationary phase selected to be long enough to allow foridentification; obtaining images of individuals of said unsortedmosquitoes in said stationary phase; electronically classifying saidindividuals from said images into at least one member of a group ofclassifications including male mosquitoes, female mosquitoes, andunclassified objects; obtaining co-ordinates of individuals of at leastone of said male mosquito and female mosquito classifications, saidcoordinates relating to a position in said stationary phase; using arobot arm to reach an individual identified by ones of said obtainedcoordinates while said individual is in said stationary phase, to storeor remove said individuals, thereby to provide sex-sorted mosquitoes. 2.The method of claim 1, wherein said classifying comprises using atrained neural network or a recurrent neural network (RNN).
 3. Themethod of claim 2, wherein said trained neural network comprises four ormore layers.
 4. The method of claim 2, wherein said obtaining imagescomprises obtaining successive frames, generating differences betweensaid successive frames and using said differences to determine whichindividuals are in said stationary phase.
 5. (canceled)
 6. The methodaccording to claim 1, wherein said unsorted insects are emerging pupaeand said stationary phase is emergence, or wherein said unsorted insectsare stationary adults, or where said unsorted insects are adults andsaid stationary phase is obtained by cooling said insects.
 7. (canceled)8. The method according to claim 1, comprising tracking movement ofindividual insects to update respective obtained coordinates prior tousing said robot arm.
 9. The method according to claim 8, comprisingobtaining said images for classification using a first, relatively highresolution, camera, and carrying out said tracking using a second,relatively low resolution, camera.
 10. The method according to claim 1,wherein said obtained coordinates are of said male class and saididentified individuals are picked off and placed in storage, or whereinsaid obtained coordinates are of said female class and said identifiedindividuals are destroyed, or wherein said obtained coordinates are ofsaid female class and said identified individuals are removed.
 11. Themethod of claim 10, wherein said robot arm comprises a suction device ora blower device to pick off said identified individuals and place instorage.
 12. (canceled)
 13. The method according to claim 10, whereinsaid robot arm comprises a zapper for destroying said identifiedindividuals, or wherein said robot arm comprises said zapper fordestroying said identified individuals and said zapper is one member ofthe group consisting of an electrode and a laser.
 14. (canceled)
 15. Themethod according to claim 1, wherein, if an individual is not classifiedinto male or female by a predetermined time, then the image is sent toan operator, or wherein if an individual is not classified as male by apredetermined time then it is classified as female.
 16. (canceled) 17.The method according to claim 6, wherein said insects are cooled in acontainer having walls, so that the cooled insects stand on an interiorside of said walls, the method comprising dismantling said box topresent said interior sides of said walls for said obtaining saidimages, or wherein said insects are cooled in a container having anopening and said opening is opened onto a first moving conveyor, toallow said cooled insects to fall through, said first moving conveyorcarrying said insects to an imaging location for said obtaining images,and said conveyor stopping with insects at said imaging location toobtain said coordinates.
 18. (canceled)
 19. The method according toclaim 17, wherein said first moving conveyor is a relatively fast movingconveyor, thereby to prevent piling of insects disrupting imaging,wherein said obtained coordinates are of the female class so that maleinsects are retained on the conveyor, the first moving conveyor emptyingonto a second moving conveyor being a relatively slow moving conveyor,said relatively slow moving conveyor conveying said retained insects forplacing in storage cartridges.
 20. Apparatus for mechanical sex-sortingof mosquitoes by extracting a class of mosquitoes from unsortedmosquitoes, the apparatus comprising: a camera configured to identifyindividual unsorted mosquitoes in a stationary phase, the stationaryphase selected to be long enough to allow for identification, the camerafurther configured to obtain images and coordinates of said individualsin said stationary phase; a classifier, configured to electronicallyclassify said individuals from said images into at least one member of agroup of classifications including male mosquitoes, female mosquitoes,and unclassified objects; a robot arm connected to said classifier andconfigured to reach an individual identified by ones of said obtainedcoordinates, the coordinates obtained while said individual is in saidstationary phase, to store or remove said individuals, thereby toprovide sex-sorted mosquitoes.
 21. The apparatus of claim 20, whereinsaid classifier comprises a trained neural network or a recurrent neuralnetwork (RNN).
 22. The apparatus of claim 21, wherein said trainedneural network comprises four or more layers.
 23. The apparatus of claim20, wherein camera is configured to obtain successive frames, and aprocessor attached to said camera generates difference frames betweensaid successive frames and using said difference frames to determinewhich individuals are in said stationary phase.
 24. (canceled)
 25. Theapparatus according to claim 20, wherein said unsorted insects areemerging pupae and said stationary phase is emergence.
 26. The apparatusaccording to claim 20, further comprising a cooler, and wherein saidunsorted insects are adults and said stationary phase is obtained bycooling said insects.
 27. The apparatus according to claim 20, furthercomprising a tracker configured to track movement of individual insectsto update respective obtained coordinates prior to using said robot arm.28. The apparatus according to claim 27, wherein said camera is arelatively high resolution, camera, and said tracker is a second,relatively low resolution, camera.
 29. The apparatus of claim 20,wherein said robot arm comprises a suction device or a blower device topick off said identified individuals and place in storage, or whereinsaid robot arm comprises a zapper for destroying said identifiedindividuals, or wherein said robot arm comprises said zapper fordestroying said identified individuals and said zapper is one member ofthe group comprising an electrode, a solenoid and a laser. 30-31.(canceled)
 32. The apparatus according to claim 20, comprising acontainer with detachable walls associated with a cooler, wherein saidinsects are cooled in said container, so that the cooled insects standon an interior side of said walls, and the box is dismantlable topresent said interior sides of said walls for said obtaining saidimages.
 33. The apparatus according to claim 20, comprising a firstmoving conveyor leading to said camera and said robot and a containerwith a trap door and a cooler, wherein said insects are cooled in saidcontainer and said trap door is opened onto said first moving conveyor,to allow said cooled insects to fall through, said first moving conveyorcarrying said insects to an imaging location below said camera for saidobtaining images, and said conveyor being configured to stop withinsects at said imaging location to obtain said coordinates.
 34. Theapparatus according to claim 33, further comprising a second movingconveyor and storage cartridges, wherein said first moving conveyor is arelatively fast moving conveyor, thereby to prevent piling of insectsdisrupting imaging, wherein said obtained coordinates are of the femaleclass so that male insects are retained on the conveyor, the firstmoving conveyor configured to empty onto said second moving conveyor,said second moving conveyor being a relatively slow moving conveyor,said second moving conveyor configured to convey said retained insectsfor placing in storage cartridges.
 35. The apparatus according to claim20, configured to track said insect, and update respective obtainedcoordinates for use by said robot arm.